Magneto-rheological dampers are an effective technology to control the damping coefficient of a semi-active suspension. Most of the contributions in literature propose damper models to be used in simulation, or as damping force virtual sensors in control applications. Typically, phenomenological models or complex black-box approaches, relying on Neural Networks, are employed. In this work, we propose a semi-active MR model based on a Hammerstein–Wiener scheme, meant not only for force estimation but also – in a more genuinely control-oriented perspective – to be proactively used in the suspension controller design. Despite being a black-box model, each component is shown to serve for the characterization of a specific feature of the MR damper, and its identification is done thanks to an ad hoc design of experiments. In particular, the Wiener part of the model is shown to be essential for the proper modelling of the magnetization dynamics of the magneto-rheological fluid, which usually is a neglected aspect in control-oriented models. The proposed scheme is validated on a testbench using realistic road solicitations.

磁流变阻尼器是控制半主动悬架阻尼系数的有效技术。文献中的大多数贡献提出了阻尼器模型，该阻尼器模型可用于仿真中，或用作控制应用中的阻尼力虚拟传感器。通常，采用依赖于神经网络的现象学模型或复杂的黑盒方法。在这项工作中，我们提出了一个基于Hammerstein-Wiener方案的半主动MR模型，该模型不仅用于力估计，而且（在更加真正的面向控制的角​​度来看）将在悬架控制器设计中主动使用。尽管是黑匣子模型，但显示了每个组件都可用于表征MR阻尼器的特定功能，并且由于特设功能，可以对其进行识别实验设计。特别是，模型的维纳部分对于正确地建模磁流变流体的磁化动力学至关重要，这在面向控制的模型中通常是被忽略的方面。拟议的方案在测试台上使用现实的道路诱因进行了验证。